[1742] - A segmentation approach for stochastic geological modeling using hidden Markov random fields

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Citation
Wang, H., Wellmann, F., Li, Z., Liang, R. Y., Wang, X., 2017. A segmentation approach for stochastic geological modeling using hidden Markov random fields. Mathematical Geosciences, 49 (2), 145 - 177. DOI: 10.1007/s11004-016-9663-9.
Identification
Title(s):Main Title: A segmentation approach for stochastic geological modeling using hidden Markov random fields
Description(s):Abstract: Stochastic modeling methods and uncertainty quantification are important tools for gaining insight into the geological variability of subsurface structures. Previous attempts at geologic inversion and interpretation can be broadly categorized into geostatistics and process-based modeling. The choice of a suitable modeling technique directly depends on the modeling applications and the available input data. Modern geophysical techniques provide us with regional data sets in two- or three-dimensional spaces with high resolution either directly from sensors or indirectly from geophysical inversion. Existing methods suffer certain drawbacks in producing accurate and precise (with quantified uncertainty) geological models using these data sets. In this work, a stochastic modeling framework is proposed to extract the subsurface heterogeneity from multiple and complementary types of data. Subsurface heterogeneity is considered as the “hidden link” between multiple spatial data sets. Hidden Markov random field models are employed to perform three-dimensional segmentation, which is the representation of the “hidden link”. Finite Gaussian mixture models are adopted to characterize the statistical parameters of multiple data sets. The uncertainties are simulated via a Gibbs sampling process within a Bayesian inference framework. The proposed modeling method is validated and is demonstrated using numerical examples. It is shown that the proposed stochastic modeling framework is a promising tool for three-dimensional segmentation in the field of geological modeling and geophysics.
Identifier(s):DOI: 10.1007/s11004-016-9663-9
Citation Advice:Wang, H., Wellmann, J. F., Li, Z., Wang, X., & Liang, R. Y. (2017). A segmentation approach for stochastic geological modeling using hidden Markov random fields. Mathematical Geosciences, 49(2), 145-177.
Responsible Party
Creator(s):Author: Hui Wang
Author: Florian Wellmann
Author: Zhao Li
Author: Robert Y. Liang
Author: Xiangrong Wang
Publisher:Springer Berlin Heidelberg
Topic
TR32 Topic:Soil
Related Sub-project(s):B9
Subject(s):CRC/TR32 Keywords: Modelling
Topic Category:GeoScientificInformation
File Details
File Name:2016_Wang_etal_A_Segmentation_Approach_for_Stochastic_Geological_model.pdf
Data Type:Text
File Size:3692 kB (3.605 MB)
Date(s):Created: 2018-05-23
Mime Type:application/pdf
Language:English
Status:Completed
Constraints
Download Permission:Free
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Geographic
North:-no map data
East:-
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Measurement Region:None
Measurement Location:--None--
Specific Informations - Publication
Status:InPrint
Review:PeerReview
Year:2017
Type:Article
Article Type:Journal
Source:Mathematical Geosciences
Issue:2
Volume:49
Page Range:145 - 177
Metadata Details
Metadata Creator:Hui Wang
Metadata Created:2018-05-23
Metadata Last Updated:2018-05-24
Subproject:B9
Funding Phase:3
Metadata Language:English
Metadata Version:V42
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